Diagnose location-sorted recommender causing revenue drop

Eats recommendations were changed to rank items primarily by distance to the user; after launch, add-to-cart rate rose but revenue per session fell. Diagnose and fix: define online and offline evaluation metrics and design both an A/B test and an offline counterfactual evaluation to separate causal from compositional effects; hypothesize mechanisms (e.g., cheaper nearby items cannibalize high-AOV items, position bias, distance–price/fee correlation, capacity throttling, promise-time effects, acceptance-rate shifts) and specify the checks you would run; propose a new ranking objective as a multi-objective optimization (expected revenue, ETA reliability, acceptance probability, fairness) with constraints and guardrails, and describe how you would add safe exploration (e.g., Thompson sampling or epsilon-greedy with caps); detail diagnostic slicing by zone/time/cohort, selection-bias controls, instrumentation to disentangle delivery-time and cancellation effects, and rollback criteria.